Workload Assessment in Remote Mixed Reality Medical Procedural Training Through Physiological Signals
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Le résumé fourni par la source
Mixed reality (MR) technology has been adopted for teaching and procedural planning/guidance in medicine, but its impact on performance is not well understood. Evaluation of cognitive load during MR-augmented learning experiences could help to quantify its utility. In this study, we performed a cognitive load analysis using physiological signals, specifically pupillary diameter (PD) and heart rate variability (HRV), to assess the learning experience of students performing simulated ultrasound-guided central venous catheterization (CVC) through MR remote assistance. We evaluated the cognitive load in students performing high-precision (vessel location and needle insertion) and low-precision (guidewire and catheter insertion) portions of the US CVC procedure and compared them to the results of students learning via teleconferencing software. The mean PD after baseline correction was lower, an indicator of decreased cognitive load, during high-precision MR-guided tasks than during the lower-precision tasks (3.80 vs 7.42 px, p<0.09). The observed changes in PD suggest a positive impact on the overall teaching experience for students exposed to holographic objects during training. Heart rate variability, measured by a ratio of low to high-frequency power, showed a similar but non-significant trend. No significant differences were seen in high vs low precision task workload in the control group. Our findings provide valuable insights into the potential benefits of incorporating holographic objects into complex medical procedure training.Clinical relevance- The study demonstrates that within MR, the cognitive load during medical procedural training is reduced when providing augmented 3D models in MR that are utilized by the instructor.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Workload Assessment in Remote Mixed Reality Medical Procedural Training Through Physiological Signals
- Date Crossref
- 15/07/2024
- Éditeur
- IEEE
- Type
- proceedings-article
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